Hunting Conspiracy Theories During the COVID-19 Pandemic
Hunting Conspiracy Theories During the COVID-19 Pandemic
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DOI：
10.1177/20563051211043212
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发表时间：
2021-07-01
影响因子：
5.2
通讯作者：
Carley, Kathleen M.
中科院分区：
文献类型：
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作者：
Moffitt, J. D.;King, Catherine;Carley, Kathleen M.
对未知的恐惧加上COVID-19造成的孤立，为强烈的虚假信息（也称为阴谋论）创造了一个肥沃的环境。因为阴谋论往往包含一个真理的核心，并以一个强大的敌对的"他者"为特征，它们是被诽谤的演员在影响力运动中使用的完美工具。为了探索阴谋在传播不实/错误信息中的重要性，我们建议使用最先进的调整语言模型来将推文分类为阴谋或不。该模型基于Google研究人员开发的双向编码器表示（BERT）模型。分类方法通过自动化目前手动完成的过程（识别宣扬阴谋论的推文）来加速分析。我们使用这种方法识别了COVID-19起源阴谋论推文，然后使用社会网络安全方法分析了不同起源相关阴谋论叙述的社区，传播者和特征。我们发现，关于阴谋论的推文得到了事实核查分数较低的新闻网站的支持，并被机器人放大，这些机器人比非阴谋推文更有可能链接到知名Twitter用户。我们还发现了阴谋与非阴谋对话在标签使用，身份和原籍国方面的不同模式。这项分析表明，我们如何更好地了解谁在传播阴谋论，以及他们是如何传播阴谋论的。
The fear of the unknown combined with the isolation generated by COVID-19 has created a fertile environment for strong disinformation, otherwise known as conspiracy theories, to flourish. Because conspiracy theories often contain a kernel of truth and feature a strong adversarial "other," they serve as the perfect vehicle for maligned actors to use in influence campaigns. To explore the importance of conspiracies in the spread of dis-/mis-information, we propose the usage of state-of-the-art, tuned language models to classify tweets as conspiratorial or not. This model is based on the Bidirectional Encoder Representations from Transformers (BERT) model developed by Google researchers. The classification method expedites analysis by automating a process that is currently done manually (identifying tweets that promote conspiracy theories). We identified COVID-19 origin conspiracy theory tweets using this method and then used social cybersecurity methods to analyze communities, spreaders, and characteristics of the different origin-related conspiracy theory narratives. We found that tweets about conspiracy theories were supported by news sites with low fact-checking scores and amplified by bots who were more likely to link to prominent Twitter users than in non-conspiracy tweets. We also found different patterns in conspiracy vs. non-conspiracy conversations in terms of hashtag usage, identity, and country of origin. This analysis shows how we can better understand who spreads conspiracy theories and how they are spreading them.